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test_aid_single.py
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# from src import architectures,ramps
# import torch.backends.cudnn as cudnn
import csv
import os
import random
import time
import warnings
from datetime import datetime
from timeit import default_timer as timer
import numpy as np
import pandas as pd
import torch
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
from torchvision import datasets
from tensorboardX import SummaryWriter
from torch import nn, optim
from torch.utils.data import DataLoader
from dataset import aid
from config.AID.config_AID import DefaultConfigs as config
from models import getnet
from utils import *
# from apex import amp
#1. set random.seed and cudnn performance
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpus
# torch.backends.cudnn.benchmark = True
warnings.filterwarnings('ignore')
obj=[
"agricultural", "airplane", "baseballdiamond", "beach", " buildings", "chaparral", "denseresidential", "forest", "freeway", "golfcourse", "harbor",
"intersection", "mediumresidential", "mobilehomepark", "overpass", "parkinglot", "river", "runway", "sparseresidential", "storagetanks", "tenniscourt"
]
#3. test model on public dataset and save the probability matrix
def test(test_loader,model):
top1 = AverageMeter(config)
top5 = AverageMeter(config)
matrix = runningScore(config=config)
matrix.reset()
times=0.0
timeall =0.0
precision1=0
precision5=0
#3.1 confirm the model converted to cuda
# progress bar
test_progressor = ProgressBar(mode="test",model_name=config.model_name, total=len(test_loader),weights=config.weights,Status=config.Status,current_time=config.time)
# 2.2 switch to evaluate mode and confirm model has been transfered to cuda
model.cuda()
model.eval()
with torch.no_grad():
for i, sample in enumerate(test_loader):
test_progressor.current = i
image, target =sample['image'],sample['label']
input = image.cuda()
target = target.cuda()
#target = Variable(target).cuda()
# 2.2.1 compute output
torch.cuda.synchronize()
start = time.time()
output= model(input)
torch.cuda.synchronize()
end = time.time()
times=end-start
timeall=timeall+times
# output=output.squeeze(2)
# output=output.squeeze(2)
# 2.2.2 measure accuracy and record loss
precision1, precision5 = accuracy(output, target, topk=(1, 5))
matrix.update(output,target)
top1.update(precision1[0],input.size(0))
# top1.perclass(class_correct,class_total)
top5.update(precision5[0], input.size(0))
test_progressor.current_top1 = top1.avg
test_progressor.current_top5 = top5.avg
test_progressor()
_, predicted = torch.max(output, 1)
# img_PIL=Image.open(origin_path).convert('RGB')
# img_NP=np.array(img_PIL)
# img_Tensor=torch.Tensor(img_NP)
#img_Tensor=loader(img_PIL).unsqueeze(0)
tag=obj[predicted.item()]
right_label=obj[target.item()]
resultdir=os.path.join(config.weights,config.model_name,config.Status,config.time)
if os.path.exists( resultdir ):
pass
else:
os.makedirs(resultdir)
f=open(resultdir+'/upload.csv','a')
csv_writer = csv.writer(f)
csv_writer.writerow([right_label,tag])
# img_path=resultdir+'/'+right_label+str(i)+'.png'
# shutil.copy(str(origin_path),img_path)
test_progressor.done()
logdir = os.path.join(config.weights,config.model_name,config.Status,config.time)
writer = SummaryWriter(logdir)
confusion_matrix=matrix.get_value()
np.save(logdir +'/confusion.npy',confusion_matrix)
# writer.add_figure('confusion matrix',figure=plot_confusion_matrix(confusion_matrix, object_names=obj, title='Not Normalized confusion matrix',normalize=False,),global_step=1)
writer.add_figure('confusion matrix',figure=plot_confusion_matrix(confusion_matrix, object_names=obj,title='Normalized confusion matrix',config=config,normalize=True),global_step=1)
# fig=plot_confusion_matrix(confusion_matrix,obj,'Test Confusion_matrix')
writer.close()
precision,recall=matrix.get_scores()
with open(os.path.join(config.weights,config.model_name,config.Status,config.time)+"/%s_test.txt"%config.model_name,"a") as f:
for i in range(config.num_classes):
print('Precision of %5s : %f %%' % (
obj[i], 100*precision[i]),file=f)
print('Recall of %5s: %f%%'%(
obj[i], 100*recall[i]),file=f)
print("Top1:%f,Top5:%f"%(top1.avg,top5.avg),file=f)
print("avg Time:",timeall*1000/len(test_loader),"ms",file=f)
#4. more details to build main function
def main():
# fold = 0
# #4.1 mkdirs
# if not os.path.exists(config.weights):
# os.mkdir(config.weights)
# if not os.path.exists(config.best_models):
# os.mkdir(config.best_models)
# if not os.path.exists(config.logs):
# os.mkdir(config.logs)
# if not os.path.exists(config.weights + config.model_name + os.sep +str(fold) + os.sep):
# os.makedirs(config.weights + config.model_name + os.sep +str(fold) + os.sep)
# if not os.path.exists(config.best_models + config.model_name + os.sep +str(fold) + os.sep):
# os.makedirs(config.best_models + config.model_name + os.sep +str(fold) + os.sep)
#4.2 get model and optimizer
model = getnet.net(config.model_name, config.num_classes,Dataset=config.dataset)
#model = torch.nn.DataParallel(model)
model.cuda()
# model = amp.initialize(model, opt_level="O1") # 这里是“欧一”，不是“零一”
#4.5 get files and split for K-fold dataset
test_data_list = aid.AID_Clean(root=config.dataroot,split='test')
test_dataloader = DataLoader(
test_data_list, batch_size=1, shuffle=True,num_workers=config.workers, pin_memory=True)
best_model = torch.load(os.path.join(config.weights,config.model_name,config.Status,config.time,'model_best.pth.tar'))
model.load_state_dict(best_model["state_dict"])
test(test_dataloader,model)
if __name__ =="__main__":
# f=open(os.path.join('/home/pc-b3-218/Code/Cls/DNet/runs/CIFAR10/50000_balanced_labels/tree.txt'))
# for i in range(14,15):
# config.data_seed=f.readline().strip()
# config.model_name=f.readline().strip()
# config.Status=f.readline().strip()
# config.time=f.readline().strip()
# # main()
# config.model_name=f.readline().strip()
# config.Status=f.readline().strip()
# config.time=f.readline().strip()
# main()
# f.close()
main()
# sum+=precision1
# avg=precision1/len(time_list)
# print("avg precision1:%f"%avg.item())